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Data:
235.10 280.70 264.60 240.70 201.40 240.80 241.10 223.80 206.10 174.70 203.30 220.50 299.50 347.40 338.30 327.70 351.60 396.60 438.80 395.60 363.50 378.80 357.00 369.00 464.80 479.10 431.30 366.50 326.30 355.10 331.60 261.30 249.00 205.50 235.60 240.90 264.90 253.80 232.30 193.80 177.00 213.20 207.20 180.60 188.60 175.40 199.00 179.60 225.80 234.00 200.20 183.60 178.20 203.20 208.50 191.80 172.80 148.00 159.40 154.50 213.20 196.40 182.80 176.40 153.60 173.20 171.00 151.20 161.90 157.20 201.70 236.40 356.10 398.30 403.70 384.60 365.80 368.10 367.90 347.00 343.30 292.90 311.50 300.90 366.90 356.90 329.70 316.20 269.00 289.30 266.20 253.60 233.80 228.40 253.60 260.10 306.60 309.20 309.50 271.00 279.90 317.90 298.40 246.70 227.30 209.10 259.90 266.00 320.60 308.50 282.20 262.70 263.50 313.10 284.30 252.60 250.30 246.50 312.70 333.20 446.40 511.60 515.50 506.40 483.20 522.30 509.80 460.70 405.80 375.00 378.50 406.80 467.80 469.80 429.80 355.80 332.70 378.00 360.50 334.70 319.50 323.10 363.60 352.10 411.90 388.60 416.40 360.70 338.00 417.20 388.40 371.10 331.50 353.70 396.70 447.00 533.50 565.40 542.30 488.70 467.10 531.30 496.10 444.00 403.40 386.30 394.10 404.10 462.10 448.10 432.30 386.30 395.20 421.90 382.90 384.20 345.50 323.40 372.60 376.00 462.70 487.00 444.20 399.30 394.90 455.40 414.00 375.50 347.00 339.40 385.80 378.80 451.80 446.10 422.50 383.10 352.80 445.30 367.50 355.10 326.20 319.80 331.80 340.90 394.10 417.20 369.90 349.20 321.40 405.70 342.90 316.50 284.20 270.90 288.80 278.80 324.40 310.90 299.00 273.00 279.30 359.20 305.00 282.10 250.30 246.50 257.90 266.50 315.90 318.40 295.40 266.40 245.80 362.80 324.90 294.20 289.50 295.20 290.30 272.00 307.40 328.70 292.90 249.10 230.40 361.50 321.70 277.20 260.70 251.00 257.60 241.80 287.50 292.30 274.70 254.20 230.00 339.00 318.20 287.00 295.80 284.00 271.00 262.70 340.60 379.40 373.30 355.20 338.40 466.90 451.00 422.00 429.20 425.90 460.70 463.60 541.40 544.20 517.50 469.40 439.40 549.00 533.00 506.10 484.00 457.00 481.50 469.50 544.70 541.20 521.50 469.70 434.40 542.60 517.30 485.70 465.80 447.00 426.60 411.60 467.50 484.50 451.20 417.40 379.90 484.70 455.00 420.80 416.50 376.30 405.60 405.80 500.80 514.00 475.50 430.10 414.40 538.00 526.00 488.50 520.20 504.40 568.50 610.60 818.00 830.90 835.90 782.00 762.30 856.90 820.90 769.60 752.20 724.40 723.10 719.50 817.40 803.30 752.50 689.00 630.40 765.50 757.70 732.20 702.60 683.30 709.50 702.20 784.80 810.90 755.60 656.80 615.10 745.30 694.10 675.70 643.70 622.10 634.60 588.00 689.70 673.90 647.90 568.80 545.70 632.60 643.80 593.10 579.70 546.00 562.90 572.50
Sample Range:
(leave blank to include all observations)
From:
To:
Number of time lags
additive
Default
5
6
7
8
9
10
11
12
24
36
48
60
Box-Cox transformation parameter (Lambda)
12
1
-2.0
-1.9
-1.8
-1.7
-1.6
-1.5
-1.4
-1.3
-1.2
-1.1
-1.0
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
Degree of non-seasonal differencing (d)
0
1
2
Degree of seasonal differencing (D)
0
1
2
Seasonality
12
1
2
3
4
6
12
CI type
White Noise
MA
Confidence Interval
Use logarithms with this base
(overrules the Box-Cox lambda parameter)
(?)
Chart options
R Code
if (par1 == 'Default') { par1 = 10*log10(length(x)) } else { par1 <- as.numeric(par1) } par2 <- as.numeric(par2) par3 <- as.numeric(par3) par4 <- as.numeric(par4) par5 <- as.numeric(par5) if (par6 == 'White Noise') par6 <- 'white' else par6 <- 'ma' par7 <- as.numeric(par7) if (par8 != '') par8 <- as.numeric(par8) ox <- x if (par8 == '') { if (par2 == 0) { x <- log(x) } else { x <- (x ^ par2 - 1) / par2 } } else { x <- log(x,base=par8) } if (par3 > 0) x <- diff(x,lag=1,difference=par3) if (par4 > 0) x <- diff(x,lag=par5,difference=par4) bitmap(file='picts.png') op <- par(mfrow=c(2,1)) plot(ox,type='l',main='Original Time Series',xlab='time',ylab='value') if (par8=='') { mytitle <- paste('Working Time Series (lambda=',par2,', d=',par3,', D=',par4,')',sep='') mysub <- paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='') } else { mytitle <- paste('Working Time Series (base=',par8,', d=',par3,', D=',par4,')',sep='') mysub <- paste('(base=',par8,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='') } plot(x,type='l', main=mytitle,xlab='time',ylab='value') par(op) dev.off() bitmap(file='pic1.png') racf <- acf(x, par1, main='Autocorrelation', xlab='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=mysub) dev.off() bitmap(file='pic2.png') rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF',sub=mysub) dev.off() (myacf <- c(racf$acf)) (mypacf <- c(rpacf$acf)) lengthx <- length(x) sqrtn <- sqrt(lengthx) load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Autocorrelation Function',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Time lag k',header=TRUE) a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','ACF(k)','click here for more information about the Autocorrelation Function'),header=TRUE) a<-table.element(a,'T-STAT',header=TRUE) a<-table.element(a,'P-value',header=TRUE) a<-table.row.end(a) for (i in 2:(par1+1)) { a<-table.row.start(a) a<-table.element(a,i-1,header=TRUE) a<-table.element(a,round(myacf[i],6)) mytstat <- myacf[i]*sqrtn a<-table.element(a,round(mytstat,4)) a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Partial Autocorrelation Function',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Time lag k',header=TRUE) a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','PACF(k)','click here for more information about the Partial Autocorrelation Function'),header=TRUE) a<-table.element(a,'T-STAT',header=TRUE) a<-table.element(a,'P-value',header=TRUE) a<-table.row.end(a) for (i in 1:par1) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,round(mypacf[i],6)) mytstat <- mypacf[i]*sqrtn a<-table.element(a,round(mytstat,4)) a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable1.tab')
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Raw Output
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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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